code: def meta_nms(dets, scores, iou_threshold):
      torch._check(
          dets.dim() == 2,
          lambda: f"boxes should be a 2d tensor, got {dets.dim()}D",
      )
      torch._check(
          dets.size(1) == 4,
          lambda: f"boxes should have 4 elements in dimension 1, got {dets.size(1)}",
      )
      torch._check(
          scores.dim() == 1,
          lambda: f"scores should be a 1d tensor, got {scores.dim()}",
      )
      torch._check(
          dets.size(0) == scores.size(0),
          lambda: f"boxes and scores should have same number of elements in dimension 0, got {dets.size(0)} and {scores.size(0)}",
      )
      ctx = torch._custom_ops.get_ctx()
      num_to_keep = ctx.create_unbacked_symint()
      return dets.new_empty(num_to_keep, dtype=torch.long)
  
file_path: vision/torchvision/_meta_registrations.py
imports: import functools
  import torch
  import torch._custom_ops
  import torch.library
  import torchvision.extension
label_desc: Use image node_id 11 for calling a global function with example usage: meta_nms(dets, scores, iou_threshold) and returns: dets